Export 1667 results:
Zhang, Q. & Chen, J., 2021. Minimum Wasserstein Distance Estimator under Finite Location-scale Mixtures. arXiv preprint arXiv:2107.01323.
Sidrow, E. et al., 2021. Modelling multi-scale, state-switching functional data with hidden Markov models. Canadian Journal of Statistics, 50(1).
Chen, J. et al., 2021. Monitoring test under nonparametric random effects model. Journal of Nonparametric Statistics, 33, pp.60-81. Available at: https://doi.org/10.1080/10485252.2021.1914337.
Syed, S. et al., 2021. Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme. Journal of Royal Statistical Society, Series B, (Accepted).
Reinhart, A. et al., 2021. An Open Repository of Real-Time COVID-19 Indicators. Proceedings of the National Academy of Sciences, 118, p.e2111452118. Available at: https://doi.org/10.1073/pnas.2111452118.
Syed, S. et al., 2021. Parallel Tempering on Optimized Paths. In International Conference on Machine Learning (ICML). International Conference on Machine Learning (ICML).
Chen, J. et al., 2021. Permutation tests under a rotating sampling plan with clustered data. Journal of nonparametric statistics, 33, pp.60-81.
Martínez, A. & Salibian-Barrera, M., 2021. RBF: An R package to compute a robust backfitting estimator for additive models. The Journal of Open Source Software, 6(60).
Boix, C.A. et al., 2021. Regulatory genomic circuitry of human disease loci by integrative epigenomics. Nature, 590, pp.300–307.
Ju, X. & Salibian-Barrera, M., 2021. Robust Boosting for Regression Problems. Computational Statistics and Data Science, 153. Available at: https://arxiv.org/abs/2002.02054.
Boente, G. & Salibian-Barrera, M., 2021. Robust functional principal components for sparse longitudinal data. Metron.
Park, Y. et al., 2021. Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders. Cold Spring Harbor Laboratory, p.2021.01.21.426000.
Chen, J. et al., 2021. Test for homogeneity with unordered paired observations. Electronic Journal of Statistics, 15, pp.1661–1694.
Wang, Y., Le, N.D. & Zidek, J.V., 2020. Approximately Optimal Spatial Design: How Good is it?. Spatial Statistics, 37, p.100409.
McDonald, D.J., 2020. Book Review: Sufficient Dimension Reduction: Methods and Applications with R. Journal of the American Statistical Association, 115. Available at: https://doi.org/10.1080/01621459.2020.1759990.
Homrighausen, D. & McDonald, D.J., 2020. Compressed and penalized linear regression. Journal of Computational and Graphical Statistics, 29, pp.309–322. Available at: https://doi.org/10.1080/10618600.2019.1660179.
Chang, B. & Joe, H., 2020. Copula diagnostics for asymmetries and conditional dependence. Journal of Applied Statistics, 47, pp.1587-1615.
Watson, J., 2020. CV.
Lee, T.Yoon, Zidek, J.V. & Heckman, N., 2020. Dimensional Analysis in Statistical Modelling. Statistical Science, p.Submitted.
Krupskii, P. & Joe, H., 2020. Flexible copula models with dynamic dependence and application to financial data. Econometrics and Statistics, 16, pp.148-167.
Chen, J., Li, P. & Liu, G., 2020. Homogeneity testing under finite location-scale mixtures. Canadian Journal of Statistics, 48, pp.670–684.
Boente, G., Salibian-Barrera, M. & Vena, P., 2020. Robust estimation for semi-functional linear regression models. Computational Statistics and Data Science, 152. Available at: https://arxiv.org/abs/2006.16156.